{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本书配套视频课程：[解剖深度学习原理，从0实现深度学习库](https://ke.qq.com/course/2900371?tuin=ac5537fd) \n",
    "\n",
    "更多代码或学习资料将向购买视频课程或书的学生提供。\n",
    "\n",
    "\n",
    "+ 博客网站：[https://hwdong-net.github.io](https://hwdong-net.github.io)\n",
    "+ youtube频道: [hwdong](http://www.youtube.com/c/hwdong)\n",
    "+ bilibili网站：[hw-dong](https://space.bilibili.com/281453312)\n",
    "\n",
    "\n",
    "## 1.1 Python快读入门\n",
    "\n",
    "#### 安装Python\n",
    "\n",
    "Ubuntu系统：\n",
    "```python\n",
    "$ sudo apt-get update\n",
    "$ sudo apt-get install python3.6\n",
    "```\n",
    "Mac系统\n",
    "```python\n",
    "brew install python3\n",
    "```\n",
    "windows系统：官网https://www.python.org/downloads 下载并运行Python3安装程序。\n",
    "\n",
    "运行python解释器：\n",
    "```\n",
    "C:\\Users\\hwdon>python\n",
    "Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] on win32\n",
    "Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n",
    ">>>\n",
    "```\n",
    "\n",
    "#### 安装jupyter notebook\n",
    "```python\n",
    "pip install --upgrade pip\n",
    "pip install jupyter\n",
    "```\n",
    "\n",
    "#### Anaconda安装工具\n",
    "\n",
    "在Anaconda官网[https://www.anaconda.com](https://www.anaconda.com) 下载并安装Anaconda即可"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.2  对象和变量\n",
    "\n",
    "#### 1.  对象\n",
    "\n",
    "用type()查询数据的类型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(3.14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(\"http://hwdong-net.github.io\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "内置函数print()函数可以输出一系列用逗号','隔开的对象，如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 3.14\n",
      "youtube频道：hwdong True\n"
     ]
    }
   ],
   "source": [
    "print(2,3.14)\n",
    "print(\"youtube频道：hwdong\",True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类型转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "<class 'int'>\n",
      "3.14\n",
      "<class 'float'>\n"
     ]
    }
   ],
   "source": [
    "print(int(3.14))             # 将3.14从float浮点类型转化为int整数类型\n",
    "print(type(int(3.14)))       #输出int(3.14)的类型\n",
    "print(float(\"3.14\"))\n",
    "print(type(float(\"3.14\")))    # 将\"3.14\"从str字符串类型转化为float浮点类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 变量\n",
    "\n",
    "给一个对象起一个名字，叫做**变量**。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.14\n",
      "6.28\n"
     ]
    }
   ],
   "source": [
    "pi = 3.14\n",
    "print(pi)\n",
    "print(2*pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "python的变量可以随时“变换大王旗”。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 3.14              #a是对象3.14的引用\n",
    "b = a                 #b和a引用同一个对象3.14\n",
    "a = \"hwdong-net.github.io\"      # a引用了新的字符串对象\"hwdong-net.github.io\"\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "内置函数input()可以用于从键盘输入一个字符串，并可以给该函数传递一个提示串。可以将输入的字符串类型转换为其他类型的数据，如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "王安\n",
      "name:  王安\n",
      "请输入分数：67.5\n",
      "score:  67.5\n"
     ]
    }
   ],
   "source": [
    "name = input()\n",
    "print(\"name: \",name)\n",
    "score = float(input(\"请输入分数：\"))  #先显示提示串，再等待输入，最后将输入字符串转化为float类型 \n",
    "print(\"score: \",score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.3  运算\n",
    "\n",
    "算数运算符："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 15\n",
    "y = 2\n",
    "print('x + y =',x+y)\n",
    "print('x - y =',x-y)\n",
    "print('x * y =',x*y)\n",
    "print('x / y =',x/y)\n",
    "print('x // y =',x//y)\n",
    "print('x ** y =',x**y)\n",
    "print('x % y =',x%y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "比较运算符==、!=、>、<、>=、<="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x > y is True\n",
      "x < y is False\n",
      "x == y is False\n",
      "x != y is True\n",
      "x >= y is True\n",
      "x <= y is False\n"
     ]
    }
   ],
   "source": [
    "x = 15\n",
    "y = 2\n",
    "print('x > y is',x>y)\n",
    "print('x < y is',x<y)\n",
    "print('x == y is',x==y)\n",
    "print('x != y is',x!=y)\n",
    "print('x >= y is',x>=y)\n",
    "print('x <= y is',x<=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "逻辑运算符and、or、not分别表示逻辑与、逻辑或、逻辑非。\n",
    "\n",
    "- 对一个对象x，当x是真 （True或非0值或非空值）时，not x就是False，当x是假（False或0或空值）时，not x就是True（真）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True True True False False\n"
     ]
    }
   ],
   "source": [
    "print(not 0,end = ' ')               #print()默认输出后换行，通过传给end参数一个空格' '，使得只输出空格而不换行\n",
    "print(not \"\",end = ' ')       \n",
    "print(not False,end = ' ')\n",
    "print(not 2,end = ' ')\n",
    "print(not \"hwdong\")   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 对于2个对象x、y，当x是真时，x or y的结果就是x，当x是假时，x or y的结果就是y。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 2 True 2\n"
     ]
    }
   ],
   "source": [
    "print(3 or 2,end = ' ')            #因为3是真，3 or 2的结果就是3\n",
    "print(0 or 2,end = ' ')            #因为0是假，0 or 2的结果就是2\n",
    "print(False or True,end = ' ') \n",
    "print(\"\" or 2)                    #因为空字符串\"\"是假，\"\" or 2的结果就是2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 对于2个对象x、y，当x是真时，x and y的结果就是y，当x是假时，x and y的结果就是x。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 0  False\n"
     ]
    }
   ],
   "source": [
    "print(3 and 2,end = ' ')           #因为3是真，3 and 2的结果就是2\n",
    "print(0 and 2,end = ' ')           #因为0是假，0 and 2的结果就是0\n",
    "print(\"\" and 2,end = ' ')          #因为空字符串\"\"是假，\"\" and 2的结果就是\"\"，因此没有输出\n",
    "print(False and True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python还有移位运算符（如位与&、位或|、异或^、取反~、左移<<、右移>>）等其他运算符。\n",
    "\n",
    "运算符=也可以与其他的算术运算符、位运算符结合使用。如：\n",
    "```\n",
    "+=  -+   *=  /=   %=   **=  &=  |=  ^=  ~= <<= >>=  \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "id()函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140705807324864\n",
      "140705807324960\n"
     ]
    }
   ],
   "source": [
    "x=2                   # 给对象2起了一个变量名x\n",
    "print(id(x))          # 内置函数id()输出一个对象的id（相当于内存地址）\n",
    "x+=3                  # 等价于 x= x+2,即变量名x指向了原来x引用的2和3相加的结果对象。\n",
    "print(id(x))      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "除id()函数外，还可以用身份运算符is和is not用于判断两个变量（标识符）是不是引用的同一个对象。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1914089469328 1914089469424 1914089469328\n",
      "False\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "a = 3.14\n",
    "b = 3.15\n",
    "c = a\n",
    "print(id(a),id(b),id(c))\n",
    "print(a is b)\n",
    "print(a is not b)\n",
    "print(a is  c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运算符in、not in用于判断一个值（对象）是否在一个容器对象中。如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "print(\"h\"in\"hwdong\")\n",
    "print(\"h\"not in\"hwdong\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.4 控制语句\n",
    "\n",
    "#### 1.  if语句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67.5\n",
      "恭喜你!\n",
      "通过了考试。\n"
     ]
    }
   ],
   "source": [
    "score = float(input())\n",
    "if score>=60:\n",
    "   print(\"恭喜你!\")\n",
    "   print(\"通过了考试。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "请输入成绩：45.5\n",
      "你未通过考试。\n",
      "继续努力，加油！\n"
     ]
    }
   ],
   "source": [
    "score= float(input(\"请输入成绩：\"));\n",
    "if score>=60:                            #如果score>=60就执行if里的程序块\n",
    "    print(\"恭喜你!\")\n",
    "    print(\"通过了考试。\")\n",
    "else:                                    #否则，执行else程序块\n",
    "    print(\"你未通过考试。\")\n",
    "    print(\"继续努力，加油！\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "请输入学生成绩：78.6\n",
      "中等\n"
     ]
    }
   ],
   "source": [
    "score= float(input(\"请输入学生成绩：\"));\n",
    "if score<60:                #如果score<60，就执行这个if程序块\n",
    "    print(\"不及格\")\n",
    "elif score<70:              #否则如果score<60，就执行这个elif程序块\n",
    "    print(\"及格\")\n",
    "elif score<80:              #否则如果score<80，就执行这个elif程序块\n",
    "    print(\"中等\")\n",
    "elif score<90:              #否则如果score<69，就执行这个elif程序块\n",
    "    print(\"良好\")\n",
    "else:                       #否则（其他情况），就执行这个else程序块\n",
    "    print(\"优秀\");     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. while语句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5050\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "s = 0\n",
    "while i<=100:\n",
    "   s = s+i;      #等价于  s += i\n",
    "   i+=1\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "请输入学生成绩：45.7\n",
      "请输入学生成绩：78.6\n",
      "请输入学生成绩：89.4\n",
      "请输入学生成绩：56.2\n",
      "请输入学生成绩：-9\n",
      "平均成绩为： 67.475\n"
     ]
    }
   ],
   "source": [
    "total_score=0\n",
    "i= 0\n",
    "score = float(input(\"请输入学生成绩：\"))\n",
    "while True:\n",
    "    total_score += score\n",
    "    i += 1\n",
    "    score = float(input(\"请输入学生成绩：\"))\n",
    "    if  score<0:\n",
    "        break                           #关键字break用于跳出循环   \n",
    "    \n",
    "print('平均成绩为：', total_score/i) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. for语句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h w d o n g "
     ]
    }
   ],
   "source": [
    "for ch in \"hwdong\":\n",
    "    print(ch,end=\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.5  Python常用容器类型\n",
    "\n",
    "#### 1.  list（列表）\n",
    "\n",
    "定义列表对象是用一对左右方括号```[ ]```包围，数据元素之间用逗号隔开。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 5, 8]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [2,5,8]\n",
    "print(a)\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "列表中元素可以是任意对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3.14, True, [3, 6, 9], 'python']\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "my_list =[2, 3.14,True,[3,6,9],'python']\n",
    "print(my_list)\n",
    "print(type(my_list))  #打印my_list的类型，即list类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 2, 3], [4, 5, 6]]\n"
     ]
    }
   ],
   "source": [
    "a = [[1,2,3],[4,5,6]]\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "和字符串一样，可以用一个下标访问其中的一个元素，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my_list[0]: 2\n",
      "my_list[3]: [3, 6, 9]\n",
      "my_list[-2]: python\n"
     ]
    }
   ],
   "source": [
    "print(\"my_list[0]:\",my_list[0])\n",
    "print(\"my_list[3]:\",my_list[3])\n",
    "print(\"my_list[-2]:\",my_list[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3.14, True, [3, 6, 9], 'python']\n",
      "[2, 3.14, True, [8, 9], 'python']\n"
     ]
    }
   ],
   "source": [
    "print(my_list)\n",
    "my_list[-2]=[8,9]\n",
    "print(my_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以用[start:end:step]访问一个列表对象的由起始下标、结束下标、步长所筛选出来元素构成的一个子列表。这种访问列表对象的方式称为切片。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, True]\n",
      "[True, [8, 9]]\n"
     ]
    }
   ],
   "source": [
    "print(my_list[0:4:2])\n",
    "print(my_list[2:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果起始下标start未指定，默认为0，如果结束下标start未指定，默认为最后一个元素的后一个位置。例如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3.14, True, [8, 9], 'python']\n"
     ]
    }
   ],
   "source": [
    "list_2 = my_list[:]  #所有元素\n",
    "print(list_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：切片操作创建的是一个新的列表对象，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1914089462592\n",
      "1914089369344\n"
     ]
    }
   ],
   "source": [
    "print(id(my_list))\n",
    "print(id(list_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果切片操作放在赋值语句的左边，表示修改该切片对应的子列表内容，如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3.14, True, [8, 9], 'python']\n",
      "[2, 3.14, 13, 9, 'python']\n"
     ]
    }
   ],
   "source": [
    "print(my_list)\n",
    "my_list[2:4] = [13, 9]\n",
    "print(my_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "和字符串一样，当然可以用for循环等访问一个list对象中的元素。如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 3.14 13 9 python "
     ]
    }
   ],
   "source": [
    "for e in my_list:\n",
    "    print(e,end=\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "甚至可以用for循环遍历一个容器或可迭代对象的方式来创建一个新的list对象。如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alist = [e**2 for e in [0,1,2,3,4,5]]\n",
    "print(alist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这种通过在[]中迭代计算产生值创建一个新list对象的式子，叫做**列表解析式**。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 4, 16]\n"
     ]
    }
   ],
   "source": [
    "alist = [0, 1, 2, 3, 4,5]\n",
    "alist = [x ** 2 for x in alist if x % 2 == 0]\n",
    "print(alist)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "内置函数range(n)是一个产生0到n之间整数（不包括n）的迭代器对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 \n",
      "[0, 1, 4, 9, 16, 25]\n"
     ]
    }
   ],
   "source": [
    "for e in range(6):\n",
    "    print(e, end = ' ')\n",
    "print()\n",
    "alist = [e**2 for e in range(6)]\n",
    "print(alist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. tuple（元组）\n",
    "\n",
    "定义tuple是用圆括号而不是方括号。如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tuple'>\n",
      "([2, 5], 37, 3.14)\n",
      "('https://hwdong.net', 3.14, 37)\n"
     ]
    }
   ],
   "source": [
    "t = ('python',[2,5],37,3.14,\"https://hwdong.net\")\n",
    "print(type(t))\n",
    "print(t[1:4])\n",
    "print(t[-1:-4:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "list中的元素是可以修改的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16, 25]\n",
      "[0, 22, 4, 9, 16, 25]\n"
     ]
    }
   ],
   "source": [
    "print(alist)\n",
    "alist[1] = 22\n",
    "print(alist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tuple中的元素是不可以被修改的，如同字符串中的元素是不可以被修改的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-70d00e4ef536>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m22\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "t[1]=22"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意**：和list不同，用()的解析式创建一个可迭代对象而不是tuple对象，如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object <genexpr> at 0x000001BDA8AE3890>\n",
      "0 1 4 9 16 25 "
     ]
    }
   ],
   "source": [
    "nums = (x**2 for x in range(6))\n",
    "print(nums) \n",
    "for e in nums:\n",
    "    print(e,end= \" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. set（集合）\n",
    "​\n",
    "set是不包含重复元素的无序集合。set是用左右花括号{}包围的以逗号隔开的一组元素。元素的类型可以是不同的。如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'set'>\n",
      "{2, 3.14, 5, 'python', 8}\n"
     ]
    }
   ],
   "source": [
    "s = {5,5,3.14,2,'python',8}\n",
    "print(type(s))\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以用add()和remove()函数向一个集合中添加和删除一个元素，而list对象可以用append()或insert()函数追加或插入元素，pop() 用于删除最后一个元素，remove()则是删除第一个指定值的元素。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{2, 3.14, 5, 'python', 8, 'hwdong'}\n",
      "{2, 3.14, 5, 'python', 8}\n",
      "[0, 22, 4, 9, 16, 25, 'hwdong']\n",
      "[0, 22, 'net', 4, 9, 16, 25, 'hwdong']\n",
      "[0, 22, 'net', 4, 9, 16, 25]\n",
      "[0, 22, 4, 9, 16, 25]\n"
     ]
    }
   ],
   "source": [
    "s.add(\"hwdong\")\n",
    "print(s)\n",
    "s.remove(\"hwdong\")\n",
    "print(s)\n",
    "alist.append(\"hwdong\")\n",
    "print(alist)\n",
    "alist.insert(2,\"net\")\n",
    "print(alist)\n",
    "alist.pop()\n",
    "print(alist)\n",
    "alist.remove(\"net\")\n",
    "print(alist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但不可修改对象如tuple则没有append()或insert()之类的函数用于添加元素。下列代码是错误的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'append'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-34fd50c7f43a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"hwdong\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'"
     ]
    }
   ],
   "source": [
    "t.append(\"hwdong\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以用{}的解析式创建一个set对象，如"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0, 1, 4, 9, 16, 25}\n"
     ]
    }
   ],
   "source": [
    "nums = {x**2 for x in range(6)}\n",
    "print(nums) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. dict（字典）\n",
    "\n",
    "dict是一个 (key-value pairs) “键-值”对的无序集合。每个元素都以“键:值(key:value)”的形式存储的。如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "{1: 'value', 'key': 2, 'hello': [4, 7]}\n"
     ]
    }
   ],
   "source": [
    "d = {1:'value', 'key':2, 'hello': [4,7]}\n",
    "print(type(d))\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过key(键，也称为关键字)才能访问dict中这个key对应元素的值value。如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 7]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['hello']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果一个key对应的元素不存在，通过这个key访问元素是非法的，如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "3",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-54-0acadf17a380>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m: 3"
     ]
    }
   ],
   "source": [
    "d[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但可以给一个不存在的key赋一个值（value），将在这个set中添加一个“键-值”对的元素。如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 'value', 'key': 2, 'hello': [4, 7], 3: 'python'}\n",
      "python\n"
     ]
    }
   ],
   "source": [
    "d[3] = \"python\"\n",
    "print(d)\n",
    "print(d[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以名字作为关键字的dict对象表示学生信息表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'LiPing': [21, '计科01', 15370203152], 'ZhangWei': [20, '计科02', 17331203312], 'ZhaoSi': [22, '机械03', 16908092516]}\n",
      "[20, '计科02', 17331203312]\n"
     ]
    }
   ],
   "source": [
    "students={\"LiPing\":[21,\"计科01\",15370203152],\"ZhangWei\":[20,\"计科02\",17331203312]\n",
    "         ,\"ZhaoSi\":[22,\"机械03\",16908092516]}\n",
    "print(students)\n",
    "print(students[\"ZhangWei\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以通过for...in循环语句访问字典中的元素，如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LiPing's info: [21, '计科01', 15370203152] \n",
      "ZhangWei's info: [20, '计科02', 17331203312] \n",
      "ZhaoSi's info: [22, '机械03', 16908092516] \n"
     ]
    }
   ],
   "source": [
    "for name in students:\n",
    "    info = students[name]\n",
    "    print('{}\\'s info: {} '.format(name, info)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果想同时获得键（key）和值（value），则可以用dict类的item()方法获得一个字典对象的所有键-值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LiPing's info: [21, '计科01', 15370203152] \n",
      "ZhangWei's info: [20, '计科02', 17331203312] \n",
      "ZhaoSi's info: [22, '机械03', 16908092516] \n"
     ]
    }
   ],
   "source": [
    "for key,value in students.items():\n",
    "    value = students[key]\n",
    "    print('{}\\'s info: {} '.format(key,value)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当然，也可以用一个{}解析式创建一个字典对象，如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}\n"
     ]
    }
   ],
   "source": [
    "points = {x:x**2 for x in range(6)}\n",
    "print(points) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.6  函数\n",
    "\n",
    "Python通过关键字**def**定义函数，给一个程序块起一个名字。如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hwdong():\n",
    "    print(\"我的youtube频道是：\",\"hwdong\")              #调用内置函数print()\n",
    "    print(\"我的B站号是：hw-dong\")\n",
    "    print(\"我的博客是：https://hwdong-net.github.io\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后就可以通过函数名去调用执行这个函数块中的代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我的youtube频道是： hwdong\n",
      "我的B站号是：hw-dong\n",
      "我的博客是：https://hwdong-net.github.io\n",
      "\n",
      "我的youtube频道是： hwdong\n",
      "我的B站号是：hw-dong\n",
      "我的博客是：https://hwdong-net.github.io\n",
      "\n",
      "我的youtube频道是： hwdong\n",
      "我的B站号是：hw-dong\n",
      "我的博客是：https://hwdong-net.github.io\n"
     ]
    }
   ],
   "source": [
    "hwdong()\n",
    "print()   #调用内置函数print()\n",
    "hwdong()\n",
    "print()   #调用内置函数print()\n",
    "hwdong()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数可以有参数，使得调用函数时，可以传递相应的参数给这个函数，如下面函数计算$x^n$："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pow(x,n):    \n",
    "    ret = 1\n",
    "    for i in range(n): #0,1,2,...,n-1\n",
    "        ret *=x # ret  = ret*x\n",
    "    return ret             # return返回函数的值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中的return称为返回语句，函数执行到return 就返回（结束函数执行），并可以返回一个值。\n",
    "\n",
    "调用这个函数，可以给函数的形式参数x和n传递2个实际的参数。如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "print(pow(3,2))    # 3.5传递给x,2传递给n\n",
    "print(pow(2,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数的参数可以有默认值，如果调用函数时，没有提供相应的参数，则这个参数就用其默认值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pow(x,n=2):   \n",
    "    ret = 1\n",
    "    for i in range(n):\n",
    "        ret *=x\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该函数的参数n默认值是2，即计算$x^2$。调用该函数时，可以传递或不传递实际参数给形式参数n。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.25\n",
      "42.875\n"
     ]
    }
   ],
   "source": [
    "print(pow(3.5))    # 3.5传递给x, n的默认值时2\n",
    "print(pow(3.5,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一个函数可以在其内部调用自身，这种函数称为**递归函数**。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fact(n):\n",
    "    if n==1:             #如果n等于1，就直接返回值1\n",
    "        return 1\n",
    "    return n * fact(n - 1)  #如果n大于1，就是n和fact(n-1)的乘积\n",
    "\n",
    "fact(4)             # 输出： 24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当然，也可以直接用循环来计算$n! = 1*2*3*\\cdots n$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fact(n):\n",
    "    ret = 1\n",
    "    i = 1\n",
    "    while i<=n:\n",
    "        ret *= i\n",
    "        i += 1\n",
    "    return ret\n",
    "\n",
    "fact(4)             # 输出： 24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### math数学包\n",
    "\n",
    "math包里定义了许多数学函数库，要使用该包的函数，需要import这个包。(导入)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4142135623730951\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "print(math.sqrt(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4142135623730951\n",
      "12.25\n"
     ]
    }
   ],
   "source": [
    "import math as mt #import... as可以给一个import的包起一个别名\n",
    "print(mt.sqrt(2))\n",
    "print(mt.pow(3.5,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "半径是2.5的圆面积和周长是：19.63,15.71\n",
      "半径是3.5的圆面积和周长是：38.48,21.99\n"
     ]
    }
   ],
   "source": [
    "import math             \n",
    "def circle(r):\n",
    "    area = math.pi*r**2\n",
    "    perimeter = 2*math.pi*r\n",
    "    return area,perimeter    \n",
    "\n",
    "area,p = circle(2.5)   \n",
    "print(\"半径是2.5的圆面积和周长是：%5.2f,%5.2f\"%(area,p))\n",
    "area,p =circle(3.5)   \n",
    "print(\"半径是3.5的圆面积和周长是：%5.2f,%5.2f\"%(area,p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数可以返回多个值，这多个值实际上是以一个tuple对象返回的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tuple'>\n",
      "19.634954084936208 15.707963267948966\n"
     ]
    }
   ],
   "source": [
    "ret = circle(2.5)  #(area,perimeter) \n",
    "print(type(ret))\n",
    "area,p = ret               \n",
    "print(area,p )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 全局变量和局部变量\n",
    "\n",
    "在函数外部定义的变量称为**全局变量**，函数内部定义的变量称为**局部变量**。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "global_x = 6\n",
    "def f():\n",
    "    x = 3\n",
    "    global_x = 5\n",
    "    print(x,global_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 5\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "f()    \n",
    "print(global_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果希望能访问全局变量，需要在函数内部用关键字global声明一个变量是全局变量，如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "global_x = 6\n",
    "def f():\n",
    "    global global_x\n",
    "    x = 3\n",
    "    global_x = 5\n",
    "    print(x,global_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 5\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "f()    \n",
    "print(global_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另外一种修改全局变量的方法就是将全局变量作为参数传递给函数，但这个全局变量必须是非基本类型的对象。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "[10, 2, 3]\n",
      "6\n",
      "[10, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "global_x = 6\n",
    "a = [1,2,3]\n",
    "\n",
    "def f(y,z):    \n",
    "    x = 3\n",
    "    y = 5\n",
    "    z[0] = 10\n",
    "    print(y)\n",
    "    print(z)\n",
    "    \n",
    "f(global_x,a)    \n",
    "print(global_x)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Anonymous/Lambda Function（匿名/lambda函数）\n",
    "\n",
    "```\n",
    "lambda arguments: expression\n",
    "```\n",
    "即省略了函数名的函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.<lambda>(x)>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambda x: x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.25"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "double = lambda x: x ** 2\n",
    "\n",
    "double(3.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 嵌套函数、闭包\n",
    "\n",
    "定义在另一个函数内部的函数称为**嵌套函数**。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n"
     ]
    }
   ],
   "source": [
    "def print_msg(msg):  #包围函数   \n",
    "    def printer():  #嵌套函数        \n",
    "        print(msg)\n",
    "    return printer  # 返回嵌套函数\n",
    "\n",
    "\n",
    "another = print_msg(\"Hello\")\n",
    "another()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "嵌套函数可以访问包围范围内的变量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_pow(n):\n",
    "    def pow(x):\n",
    "        return x ** n  #pow()可以访问make_pow()的变量（即n）。\n",
    "    return pow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "729\n",
      "243\n",
      "32768\n"
     ]
    }
   ],
   "source": [
    "pow3 = make_pow(3)         \n",
    "\n",
    "pow5 = make_pow(5)\n",
    "\n",
    "print(pow3(9))\n",
    "print(pow5(3))\n",
    "print(pow5(pow3(2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### yield和生成器\n",
    "生成器是一个可以返回一个迭代器(对象)的函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def infinite_sequence():\n",
    "    num = 0\n",
    "    while True:\n",
    "        yield num\n",
    "        num += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 "
     ]
    }
   ],
   "source": [
    "for i in infinite_sequence():\n",
    "      print(i, end=\" \")\n",
    "      if i>20:\n",
    "             break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.7  类和对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前面的str、list等都是类。一般的，可以通过类名创建一个类对象，类对象是一个具体的对象。如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "7\n",
      "[0, 1, 2, 3, 4, 5]\n",
      "[0, 1, 20, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "s = str(\"http://hwdong.net\")\n",
    "print(type(s)) #str\n",
    "location = s.find(\"hwdong\")     #通过str的find()方法查询是否存在一个子串，返回子串的位置\n",
    "print(location)\n",
    "alist = list(range(6))  #[0,1,2,3,4,5]  list\n",
    "blist = alist.copy()          #通过list的copy()方法产生alist修地一个复制（拷贝）list\n",
    "blist[2] = 20\n",
    "print(alist)\n",
    "print(blist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python中用关键字class定义一个类。为了刻画所有学生的共同属性，可以定义一个Student类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Student:\n",
    "    def __init__(self, name, score):\n",
    "        self.name = name\n",
    "        self.score = score\n",
    "        \n",
    "    def print(self):\n",
    "        print(self.name,\",\",self.score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类的中__init__()方法是一个特殊方法，称为**构造函数**，在定义类的对象时，会自动调用这个构造函数，对self指向的类对象进行初始化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LiPing , 67\n",
      "WangQiang , 83\n"
     ]
    }
   ],
   "source": [
    "s1 = Student(\"LiPing\",67)\n",
    "s2 = Student(\"WangQiang\",83)\n",
    "s1.print()\n",
    "s2.print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类属性是类的所有对象都共享的属性。Student类添加了一个类属性count："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Student:\n",
    "    count=0\n",
    "    def __init__(self, name, score):\n",
    "        self.name = name\n",
    "        self.score = score\n",
    "        Student.count +=1\n",
    "        \n",
    "    def print(self):\n",
    "        print(self.name,\",\",self.score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "print(Student.count)\n",
    "s1 = Student(\"LiPing\",67)\n",
    "print(s1.count)\n",
    "s2 = Student(\"WangQiang\",83)\n",
    "print(Student.count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "Matplotlib是一个用于2D图形的Python软件包，可以用来绘制图形或显示图像。\n",
    "windows系统下以管理员权限打开命令行窗口，然后执行下列命令安装它。\n",
    "```\n",
    "pip install matplotlib\n",
    "```\n",
    "Linux或Mac需要管理员权限，即执行：\n",
    "```\n",
    "sudo pip install matplotlib\n",
    "```\n",
    "\n",
    "matplotlib.pyplot为matplotlib面向对象的绘图库提供了方便的类似Matlab的接口函数。可以用下面的代码导入matplotlib.pyplot模块并命名为plt，可避免在代码中写一长串的matplotlib.pyplot。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以直接绘制1D数据，如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [i*0.5 for i in range(10)]\n",
    "print(y)\n",
    "plt.plot(y)           #  绘制y作为纵轴坐标点构成的图形\n",
    "plt.show()            #  调用plt.show()显示图形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制2D数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0.00', '0.10', '0.20', '0.30', '0.40', '0.50', '0.60', '0.70', '0.80', '0.90']\n",
      "['0.00', '0.01', '0.04', '0.09', '0.16', '0.25', '0.36', '0.49', '0.64', '0.81']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [i*0.1 for i in range(10)]\n",
    "y = [xi**2 for xi in x]\n",
    "print([\"{0:0.2f}\".format(i) for i in x])\n",
    "print([\"{0:0.2f}\".format(i) for i in y])\n",
    "plt.plot(x, y)        #  绘制(x,y)坐标点构成的图形\n",
    "plt.show()            #  调用plt.show()显示图形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同时绘制几个曲线。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [i*0.2 for i in range(10)]\n",
    "y = [xi**2 for xi in x]\n",
    "y2 = [3*xi-1 for xi in x]\n",
    "\n",
    "plt.plot(x, y)        #  绘制(x,y)坐标点构成的图形\n",
    "plt.plot(x, y2)  \n",
    "plt.ylim(0,5)\n",
    "plt.xlabel('x axis label')\n",
    "plt.ylabel('y axis label')\n",
    "plt.title('y=x^2 and y=3x-1')\n",
    "plt.legend(['y=x^2', 'y=3x-1'])   #  指定图例的标签labels\n",
    "plt.show()                        #  调用plt.show()显示图形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot()函数还可以接受一些参数，用于定制绘制的图形的样式，如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "x = [i*0.2 for i in range(50)]\n",
    "y = [math.sin(xi) for xi in x]\n",
    "y2 = [math.cos(xi) for xi in x]\n",
    "y3 = [0.2*xi for xi in x]\n",
    "plt.plot(x, y,'r-')        \n",
    "plt.plot(x, y2,'bo')  \n",
    "plt.plot(x, y3,'g:')  \n",
    "plt.legend(['sin(x)', 'cos(x)','0.2x'])\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scatter()用于绘制散乱点图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "x = [i*0.2 for i in range(50)]\n",
    "y = [math.sin(xi) for xi in x]\n",
    "y2 = [math.cos(xi) for xi in x]\n",
    "y3 = [0.2*xi for xi in x]\n",
    "plt.scatter(x, y, c='r', s=6, alpha=0.2)        \n",
    "plt.scatter(x, y2,c='g', s=18, alpha=0.9)  \n",
    "plt.scatter(x, y3,c='b', s=3, alpha=0.4)  \n",
    "plt.legend(['sin(x)', 'cos(x)','0.2x'])\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中参数c表示颜色，其值'r'、'g'、'b'分别表示红、绿、蓝，s参数表示点的大小，alpha表示图的透明性。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### subplot()\n",
    "\n",
    "可以用subplot()函数指定子图绘制在那个子图窗口中。\n",
    "```\n",
    "subplot(numRows, numCols, plotNum)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "x = [i*0.2 for i in range(50)]\n",
    "y = [math.sin(xi) for xi in x]\n",
    "y2 = [math.cos(xi) for xi in x]\n",
    "y3 = [0.2*xi for xi in x]\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(12, 4, forward=True)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x, y,'r-') \n",
    "plt.plot(x, y2,'bo')  \n",
    "plt.title('sin(x) and cos(x)')\n",
    "plt.legend(['sin(x)', 'cos(x)'])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x, y3,'g:')  \n",
    "plt.title('0.2x') \n",
    "\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Axes objects\n",
    "\n",
    "subplot()函数返回一个axes对象。我们可以使用它随时指定哪个subplot(子图)处于活动状态："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from math import pi\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "x = [i*0.03 for i in range(300)]\n",
    "y = [math.sin(2*xi) for xi in x]\n",
    "y2 = [math.sin(10*xi) for xi in x]\n",
    "y3 =[math.cos(2*xi) for xi in x]\n",
    "\n",
    "plt.subplots_adjust(hspace=0.4)\n",
    "\n",
    "ax1 = plt.subplot(2,1,1) # subplot(2,1,1) is active, plotting will be done there\n",
    "plt.xlim(0, 9)\n",
    "plt.plot(x, y)\n",
    "plt.title('subplot(2,1,1)')\n",
    "\n",
    "ax2 = plt.subplot(2,1,2) # subplot(2,1,2) is now active\n",
    "plt.xlim(0, 9)\n",
    "plt.plot(x, y2)\n",
    "plt.title('subplot(2,1,2)')\n",
    "\n",
    "plt.axes(ax1)            # we activate subplot(2,1,1) to do more plotting on this subplot\n",
    "plt.plot(x, y3, 'r--')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### mplot3d\n",
    "\n",
    "与其他axes(轴)一样，使用projection ='3d'关键字创建Axes3D对象。创建一个 matplotlib.figure.Figure，并添加一个类型 Axes3D的axes(轴)："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以用Axes3D的方法plot()、scatter()、plot_wireframe()、plot_surface()去画线、画点、画线框、画阴影曲面。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.rcParams['legend.fontsize'] = 10\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)\n",
    "z = np.linspace(-2, 2, 100)\n",
    "r = z**2 + 1\n",
    "x = r * np.sin(theta)\n",
    "y = r * np.cos(theta)\n",
    "ax.plot(x, y, z, label='parametric curve')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def randrange(n, vmin, vmax):\n",
    "    '''\n",
    "    Helper function to make an array of random numbers having shape (n, )\n",
    "    with each number distributed Uniform(vmin, vmax).\n",
    "    '''\n",
    "    return (vmax - vmin)*np.random.rand(n) + vmin\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "n = 100\n",
    "\n",
    "# For each set of style and range settings, plot n random points in the box\n",
    "# defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh].\n",
    "for c, m, zlow, zhigh in [('r', 'o', -50, -25), ('b', '^', -30, -5)]:\n",
    "    xs = randrange(n, 23, 32)\n",
    "    ys = randrange(n, 0, 100)\n",
    "    zs = randrange(n, zlow, zhigh)\n",
    "    ax.scatter(xs, ys, zs, c=c, marker=m)\n",
    "\n",
    "ax.set_xlabel('X Label')\n",
    "ax.set_ylabel('Y Label')\n",
    "ax.set_zlabel('Z Label')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120\n",
      "[[-30.  -29.5 -29.  ...  28.5  29.   29.5]\n",
      " [-30.  -29.5 -29.  ...  28.5  29.   29.5]\n",
      " [-30.  -29.5 -29.  ...  28.5  29.   29.5]\n",
      " ...\n",
      " [-30.  -29.5 -29.  ...  28.5  29.   29.5]\n",
      " [-30.  -29.5 -29.  ...  28.5  29.   29.5]\n",
      " [-30.  -29.5 -29.  ...  28.5  29.   29.5]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import axes3d\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Grab some test data.\n",
    "X, Y, Z = axes3d.get_test_data(0.05)\n",
    "print(len(X))\n",
    "print(X)\n",
    "\n",
    "# Plot a basic wireframe.\n",
    "ax.plot_wireframe(X, Y, Z, rstride=10, cstride=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "# Make data.\n",
    "X = np.arange(-5, 5, 0.25)\n",
    "Y = np.arange(-5, 5, 0.25)\n",
    "X, Y = np.meshgrid(X, Y)\n",
    "R = np.sqrt(X**2 + Y**2)\n",
    "Z = np.sin(R)\n",
    "\n",
    "# Plot the surface.\n",
    "surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,\n",
    "                       linewidth=0, antialiased=False)\n",
    "\n",
    "# Customize the z axis.\n",
    "ax.set_zlim(-1.01, 1.01)\n",
    "ax.zaxis.set_major_locator(LinearLocator(10))\n",
    "ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
    "\n",
    "# Add a color bar which maps values to colors.\n",
    "fig.colorbar(surf, shrink=0.5, aspect=5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
